Factors that predict brook trout distribution, thermal habitat, and abundance in Northwestern Ontario streams
Bibliographic record
Abstract
Predictive models were developed to improve the understanding of \nstream-resident brook trout (Salvelinus fontinalis) populations and habitat in \nnorthwestern Ontario, and to facilitate protection of stream-resident brook trout \nfrom the adverse impacts of timber harvest. Geology-based models correctly \npredicted trout presence/absence in 75%-80% of streams studied in 1993. \nHowever, correct prediction rates declined to 50%-65% when these models \nwere transferred to independent data collected in 1992 and 1994. Combining \ndata from all years produced models that correctly predicted trout \npresence/absence in 70%-80% of streams. Univariate geology models were \nbest at predicting trout presence (up to 85% correct predictions). One-third of \nthe trout streams data had maximum summer temperatures >22deg.C , and thus \nare considered marginal. Using the combined data, models with geology and \nclimate variables explained up to 24% of the variation associated with stream \ntemperatures. Stream temperatures were negatively related to brook trout \nabundance in the combined data. Stability of stream temperatures accounted \nfor 25% of the variation in trout biomass (kg/ha). These models could be used \nby fisheries managers to implement current guidelines protecting brook trout \nhabitat from the effects of timber harvest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".